Spark ML - Multilayer Perceptron
ml_multilayer_perceptron_classifier
Description
Classification model based on the Multilayer Perceptron. Each layer has sigmoid activation function, output layer has softmax.
Usage
ml_multilayer_perceptron_classifier(
x,
formula = NULL,
layers = NULL,
max_iter = 100,
step_size = 0.03,
tol = 1e-06,
block_size = 128,
solver = "l-bfgs",
seed = NULL,
initial_weights = NULL,
thresholds = NULL,
features_col = "features",
label_col = "label",
prediction_col = "prediction",
probability_col = "probability",
raw_prediction_col = "rawPrediction",
uid = random_string("multilayer_perceptron_classifier_"),
...
)
ml_multilayer_perceptron(
x,
formula = NULL,
layers,
max_iter = 100,
step_size = 0.03,
tol = 1e-06,
block_size = 128,
solver = "l-bfgs",
seed = NULL,
initial_weights = NULL,
features_col = "features",
label_col = "label",
thresholds = NULL,
prediction_col = "prediction",
probability_col = "probability",
raw_prediction_col = "rawPrediction",
uid = random_string("multilayer_perceptron_classifier_"),
response = NULL,
features = NULL,
...
)Arguments
| Arguments | Description |
|---|---|
| x | A spark_connection, ml_pipeline, or a tbl_spark. |
| formula | Used when x is a tbl_spark. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details. |
| layers | A numeric vector describing the layers - each element in the vector gives the size of a layer. For example, c(4, 5, 2) would imply three layers, with an input (feature) layer of size 4, an intermediate layer of size 5, and an output (class) layer of size 2. |
| max_iter | The maximum number of iterations to use. |
| step_size | Step size to be used for each iteration of optimization (> 0). |
| tol | Param for the convergence tolerance for iterative algorithms. |
| block_size | Block size for stacking input data in matrices to speed up the computation. Data is stacked within partitions. If block size is more than remaining data in a partition then it is adjusted to the size of this data. Recommended size is between 10 and 1000. Default: 128 |
| solver | The solver algorithm for optimization. Supported options: “gd” (minibatch gradient descent) or “l-bfgs”. Default: “l-bfgs” |
| seed | A random seed. Set this value if you need your results to be reproducible across repeated calls. |
| initial_weights | The initial weights of the model. |
| thresholds | Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class’s threshold. |
| features_col | Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by ft_r_formula. |
| label_col | Label column name. The column should be a numeric column. Usually this column is output by ft_r_formula. |
| prediction_col | Prediction column name. |
| probability_col | Column name for predicted class conditional probabilities. |
| raw_prediction_col | Raw prediction (a.k.a. confidence) column name. |
| uid | A character string used to uniquely identify the ML estimator. |
| … | Optional arguments; see Details. |
| response | (Deprecated) The name of the response column (as a length-one character vector.) |
| features | (Deprecated) The name of features (terms) to use for the model fit. |
Details
ml_multilayer_perceptron() is an alias for ml_multilayer_perceptron_classifier() for backwards compatibility.
Value
The object returned depends on the class of x. If it is a spark_connection, the function returns a ml_estimator object. If it is a ml_pipeline, it will return a pipeline with the predictor appended to it. If a tbl_spark, it will return a tbl_spark with the predictions added to it.
See Also
Other ml algorithms: ml_aft_survival_regression(), ml_decision_tree_classifier(), ml_gbt_classifier(), ml_generalized_linear_regression(), ml_isotonic_regression(), ml_linear_regression(), ml_linear_svc(), ml_logistic_regression(), ml_naive_bayes(), ml_one_vs_rest(), ml_random_forest_classifier()
Examples
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
partitions <- iris_tbl %>%
sdf_random_split(training = 0.7, test = 0.3, seed = 1111)
iris_training <- partitions$training
iris_test <- partitions$test
mlp_model <- iris_training %>%
ml_multilayer_perceptron_classifier(Species ~ ., layers = c(4, 3, 3))
pred <- ml_predict(mlp_model, iris_test)
ml_multiclass_classification_evaluator(pred)